Spatial data analysis, multiregional modeling and macroeconomic growth by Attila Varga

1. Spatial data analysis, multiregional modeling and macroeconomic growth by Attila Varga Center for Research in Economic Policy (GKK) and Department of Economics University of Pécs, Hungary

2. Introduction • A-spatial mainstream growth theory • K, L and A only? How about their spatial arrangements? • Why should we care about space? - Transport cost (evident, but can be integrated) - Spatial externalities (requires a different approach) • Policy relevance (EU)

3. Outline • Introduction • Technological progress, spatial structure and macroeconomic growth – An empirical modeling framework • Geographical growth studies - methodological issues: • Dependence in space: Spatial data analysis in knowledge spillover research • Spatial macro modeling: Integrating macro and regional levels • Endogenizing spatial structure • Summary

4. Technological progress, spatial structure and macroeconomic growth Complex issue treated in three separate fields of economics: A. EG: “Endogenous economic growth” models: endogenized technological change in growth theory (Romer 1986, 1990, Lucas 1986, Aghion and Howitt 1998) in Romer (1990): • for-profit private R&D • knowledge spillovers and growth • rate of technical change equals rate of per-capita growth on the steady state • Simplistic explanation of technological progress, no geography

5. Technological progress, spatial structure and macroeconomic growth B. IS: „Systems of innovation”literature: innovation is an interactive process among actors of the system (Lundval 1992, Nelson 1993) actors of the IS: - innovating firms - suppliers, buyers - industrial research laboratories - public (university) research institutes - business services - “institutions” level of innovation depends on: - the knowledge accumulated in the system - the interactions (knowledge flows) among the actors - codified, non-codified (tacit) knowledge and the potential significance of spatial proximity - does not say anything about geography and growth

6. Technological progress, spatial structure and macroeconomic growth C. NEG: “New economic geography”models: endogenized spatial economic structure in a general equilibrium model (Krugman 1991, Fujita, Krugman andVenables 1999, Fujita andThisse 2002) - spatially extended Dixit-Stiglitz framework - increasing returns, monopolistic competition - spatial structure depends on some parameter conditions that determine the equilibrium level of centrifugal and centripetal forces - „cumulative causation” - C-P model by Krugman: still the point of departure - models quickly become complex: simulations if analytical solutions are not accessible - Technological change not explained (not even included until very recently), the study of its relation to growth is a recent phenomenon

7. Technological progress, spatial structure and macroeconomic growth • Theoretical integration: endogenous growth and new economic geography (Baldwin and Forslid 2000, Fujita and Thisse 2002, Baldwin et al. 2003) • EG, IS, NEG: methodological problems in THEORETICAL integration (dramatically diverging initial assumptions, different theoretical structures, research methodologies) • EMPIRICALintegration: very few work (Ciccone and Hall 1996, Varga and Schalk 2004, Acs and Varga 2004)

8. Technological progress, spatial structure and macroeconomic growth: an empirical modeling framework • Starting points: • Technological change is a collective process that depends on accumulated knowledge and interactions (IS) • Technological change is the simple most important determinant of economic growth (EG) • Codified and tacit knowledge: different channels of spillovers (the „geography of innovation” literature) • Centripetal and centrifugal forces shape geographical structure via cumulative processes (NEG) • The resulting geographic structure is a determinant of the rate of growth (NEG)

9. Technological progress, spatial structure and macroeconomic growth: an empirical modeling framework • Y = AKαLβ(EG) • The Romer (1990) equation as in Jones (1995) dA =  HAAφ, - HA: the number of researchers(“person-embodied”, codifiable/tacit knowledge component of knowledge production) - A: the total stock of technological knowledge (codified knowledge component of knowledge production) - dA: the change in technological knowledge - : the “research productivity parameter”(0<<1) φ: “codified knowledge spillovers parameter” - reflects spillovers with unlimited spatial accessibility : the “research spillovers parameter” - reflects localized knowledge spillover effects - regional and urban economics and the new economic geography suggest:  increases with geographic concentration of economic activities

10. Technological progress, spatial structure and macroeconomic growth: an empirical modeling framework Eq.1 Regional knowledge production Kr = K (RDr, URDr, Zr) Eq.2 Agglomeration effect – RD spillovers ∂Kr/∂RDr = f (RDr, URDr, Zr) Eq.3 R&D location dRDr = R(∂Kr/∂RDr) Eq.4 Geography and   =  (GSTR(HA)) Eq.5 dA =  HAγ Aφ Eq.6 dy/y = H(dA, ZN)

11. Empirical research on geography, technology and growth: 1986-2004 1986-2004: 253 papers on the geography of knowledge spillovers journal articles: 175 books, book chapters, working papers: 78

12. Geographical growth studies - methodological issues

13. Geographical growth studies - methodological issues I. Dependence in space: Spatial data analysis in knowledge spillover research II. Spatial macro modeling: Integrating macro and regional levels III. Endogenizing spatial structure

14. I. Dependence in space: Spatial data analysis in knowledge spillover research The spatial distribution of US innovations, 1982

15. I. Dependence in space: Spatial data analysis in knowledge spillover research • Tendency of innovation to cluster in space • Clustering is a consequence of dependence among spatial units • Spatial dependence makes traditional econometric techniques no longer appropriate (Anselin 1988, 2001) • Spatial data analysis: • Exploratory spatial data analysis (ESDA) • Spatial econometrics

16. I. Dependence in space: Spatial data analysis in knowledge spillover research • ESDA: global and local measures of spatial dependence • Global measures – general form: G = Si,j wij cij • Local measures: • Moran Scatterplot • Local Moran

17. Moran Scatterplot

18. Local Moran statistics

19. I. Dependence in space: Spatial data analysis in knowledge spillover research • Spatial econometrics: models with high intuitive value to study spatial knowledge spillovers • Basis: innovation equation in a form of a classical linear regression: y = Xb + e where: y: innovation output; x inputs to innovation • Modeling geographical spillovers – two main issues (Anselin 2003): A. their spatial extent (local or global) B. direct or indirect modeling

20. I. Dependence in space: Spatial data analysis in knowledge spillover research • Modeling the spatial extent of spillovers: A.1. global autocorrelation modelling e = lWe + u = [I - lW]-1 u A.2. local autocorrelation modelling e = [I + gW] u

21. I. Dependence in space: Spatial data analysis in knowledge spillover research • Direct or indirect modelling – the most commonly used solutions: B.1. Direct modelling (the „spatial lag model”): y= (I - rW)-1 Xb + (I-lW)-1 u = rWy + Xb + u B.2. Indirect modelling (the „spatial error model”) y= Xb + (I-lW)-1 u

22. The facts: spatial econometrics in empirical innovation research

23. Spatial econometrics: Facts, needs and opportunities • Urgent need for extending the toolbox: spatial logit, probit, Tobit, Poisson, panel • User-friendly softwares with support • New intermediate level textbook with applications

24. II. Spatial macro modeling: Integrating macro and regional levels • Q: how to integrate eqs (1) to (3) (regional level) with eqs (5) and (6) by eq (7) empirically? • An example: the EcoRET model (Schalk and Varga 2004, Varga and Schalk 2004)

25. EcoRET: The main characteristics • macroEconometric model with Regionally Endogenized Technological change • General features (cost minimization; vintage capital production function; technology and labor/capital demand, output; goods markets; final demand) • Geography and technology development: the conceptual basis - New economic geography - Endogenizing technological change in “endogenous economic growth” models (Romer 1986, 1990, Lucas 1986, Aghion and Howitt 1998) - The geography of knowledge spillovers (Jaffe, Trajtenberg and Henderson 1993, Audretsch and Feldman 1996, Anselin, Varga and Acs 1997)

26. EcoRET: The modeling framework • Structure of EcoRET – four blocks: • The supply side block (labor market, production, productivity, investment, employment and unemployment, production costs, inflation) • The demand side block (behavioral relationship of private households, consumption, and other components of final demand (government consumption, foreign trade etc.) in real and nominal terms and their deflators) • The income distribution block (determining private and government income - labor and property income, profits - and the transfers of income between private households and the government - taxes, social security and other transfers) • The Total Factor Productivity (TFP) block (modeling changes in regional level TFP as a function of certain knowledge-related variables as well as CSF measures such as promotion of physical infrastructure and human capital) EcoRET consists of: 106 variables, 32 of them are explained by behavioral or technical relationships, 16 variables are exogenous while the remainder of the endogenous variables is explained by definitional identities

27. EcoRET: Data and estimation • Various Hungarian (Hungarian Central Statistical Office, Hungarian Patent Office) and international (OECD, IMF) data sources • For the period of 1990 - 2000 • Units of observation: - country (macromodel) - counties (technology model) • Parameters - estimation/calibration (macromodel) - pooled estimation (technology model)

28. EcoRET: The regional TFP block The estimated regional model of technological change TFPGR = α0 + α1KNAT + α2RD+ α3KIMP + α4INFRAINV + α5HUMCAPINV + ε, TFPGR: the annual rate of growth of Total Factor Productivity (TFP), KNAT: domestically available technological knowledge accessible with no geographical restrictions (measured by stock of patents), RD: private and public regional R&D, KIMP: imported technologies (measured by FDI), INFRAINV: investment in physical infrastructure, HUMCAPINV: investment in human capital, region i and time t α1 estimates domestic knowledge spillover effects α2 estimates localized (regional) knowledge spillover effects α3 estimates international knowledge spillover effects

29. EcoRET: Linking the TFP block to the rest of EcoRET in policy simulations • Problem: - Macro blocks: time series estimation - TFP block: time-space data • Literature: agglomeration and technological change (Feldman 1994, Fujita and Thisse 2002, Varga 2000) • Solution: weighted averaged county TFP growth rates (Excellent historical forecast of national level TFP!) • The linkage: TFP = TFP-1eeDNTFPGR

30. EcoRET: Simulated effect of the geography of CSF support on the national growth rate • The ratios of the growth effects of concentrating CSF resources in: • leading areas (LEAD/LAG) • lagging areas (LAG/EQUAL) • equal distribution (LEAD/EQUAL)

31. III. Endogenizing spatial structure • Q: How to endogenize and integrate: equation (3), the R&D location equation, i.e., the long run spatial effects? • A promising solution is to integrate Spatial Computable Equilibrium (SCGE) models (to endogenize R&D distribution) with macroeconometric models to simulate the macroeconomic growth effects.

32. Summary • An empirical modeling framework is presented • Methodological reasons for a relative negligence of the spatial aspects of macroeconomic growth are reviewed: • Challenges in spatial data analysis • Difficulties in integrating regional and macro levels • Complications in endogenizing spatial structure in empirical macroeconomic growth models